Subpixel Batch Upsample Op Tests

class MyOp(theano.Op):
    # Properties attribute


    #itypes and otypes attributes are
    #compulsory if make_node method is not defined.
    #They're the type of input and output respectively
    itypes = [cuda.CudaNdarrayType([False,False,False,False])]
    otypes = [cuda.CudaNdarrayType([False]*4)]
    

   
    def __init__(self,r):
        self.r = r
        super(MyOp,self).__init__()
        


    
    def perform(self, node, inputs, output_storage):
        
        input = inputs[0]
        input_shape=input.shape    


        out = T.zeros((input_shape[0]//self.r**2,input_shape[1],self.r*input_shape[2],self.r*input_shape[3]))
        batch_size = input.shape[0]//self.r**2
 
        for x in xrange(self.r): # loop across all feature maps belonging to this channel
            for y in xrange(self.r):
                out=T.set_subtensor(out[:,:,x::self.r,y::self.r],input[batch_size*(self.r*x+y):batch_size*(self.r*x+y+1),:,:,:])
        return out


    # Other type of implementation
    def infer_shape(self,node, input_shapes):
        input_shape = input_shapes[0]
        return [(input_shape[0]//self.r**2,input_shape[1],self.r*input_shape[2],self.r*input_shape[3])]


    def grad(self, inputs, output_grads):

        out = output_grads[0]
        return T.concatenate([out[:,:,x::self.r,y::self.r] for x in xrange(self.r) for y in xrange(self.r)],axis=0)